Association of County Race and Ethnicity Characteristics With Number of Insurance Carriers and Insurance Network Breadth

Key Points Question Are strategic decisions made by insurers in the Affordable Care Act health exchanges about what markets to enter and what physicians to include within their networks associated with the underprovision of insurance plans and in-network options in areas with higher non-Hispanic Black population shares? Findings In this cohort study of health insurance exchange data with county and census tract controls, a 1-SD increase in county non-Hispanic Black population was associated with a 14.1% reduction in the number of insurers offering plans, and a 1-SD increase in the census-tract non-Hispanic Black population was associated with a 15.8% to 24.7% reduction in physicians’ insurance network participation. Meaning These findings suggest that strategic decisions by insurers in the Affordable Care Act marketplaces may contribute to fewer insurance options being available in markets with higher racial or ethnic minority populations.


A. Additional Details on the Specification of Regression Models
The regression model specification for our first set of (county level) analyses is given by: where captures the number of health insurance carriers within county c, is the non-Hispanic black prevalence in a given county, denotes the other county level controls (previously described), and captures the state specific indicators.
The regression model for the second set of (census tract) analysis is given by: .
where . ℎ is the average provider network breadth (for a given specialty) within census-track region r. All other variables in equation (2) are similarly defined to those in equation (1), but at the census tract (rather than county) level.

Carriers Count Used within Main County Market Participation Analysis
As an additional robustness check, we drew on carrier specific enrollment data from the individual exchanges in 2014 to construct Herfindahl-Hirschman Indices (HHI) for each county. The CMS issuer enrollment data that we used to construct this measure has a limitation in that it censors enrollment figures for plans with 10 or fewer enrolls within a given county. Given this limitation, we employ three different imputation approaches: (1) impute censored enrollment values with "0"; (2) impute censored enrollment values with "5"; and (3) impute censored enrollment values with "10". Once these enrollment figures were imputed we computed county specific market shares for each insurer and then used these to compute the HHI (which is theoretically bound between 1 = monopoly, and 0 = perfect competition).
Results from when we replaced our "Number of Health Insurance Carriers" count outcome with the HHI are provided within eTable 4. Looking at eTable 4 two things stand out. First, we note that the results are qualitatively similar when we use the HHI as our main outcome measure (instead of using the insurer count measure). That is, the results indicate that areas with higher non-Hispanic black population shares are significantly associated with less competitive, and more concentrated, insurance carrier markets (p<0.01 across all specifications). Second, we note similar results across all three sets of imputation approaches. This suggest that these findings are robust to alternative imputation strategies for when small enrollments are censored.

Carriers Count Used within Main County Market Participation Analysis
As an additional robustness check, we drew on carrier specific enrollment data from the individual exchanges in 2014 to construct Herfindahl-Hirschman Indices (HHI) for each county. The CMS issuer enrollment data that we used to construct this measure has a limitation in that it censors enrollment figures for plans with 10 or fewer enrolls within a given county. Given this limitation, we employ three different imputation approaches: (1) impute censored enrollment values with "0"; (2) impute censored enrollment values with were imputed we computed county specific market shares for each insurer and then used these to compute the HHI (which is theoretically bound between 1 = monopoly, and 0 = perfect competition).
Results from when we replaced our "Number of Health Insurance Carriers" count outcome with the HHI are provided within eTable 4. Looking at eTable 4 two things stand out. First, we note that the results are qualitatively similar when we use the HHI as our main outcome measure (instead of using the insurer count measure). That is, the results indicate that areas with higher non-Hispanic black population shares are significantly associated with less competitive, and more concentrated, insurance carrier markets (p<0.01 across all specifications). Second, we note similar results across all three sets of imputation approaches. This suggest that these findings are robust to alternative imputation strategies for when small enrollments are censored.

Robustness to An Alternative Quadratic Functional Form Specification
Figure 1 within the main text suggests that the association between markets having one health insurance carriers and two, or three, health insurance carries within a given market is visibly associated with the non-Hispanic black prevalence of the county in question. However, the effect of this seems less pronounced for markets with larger number of competitors. This suggest that an alternative (quadratic) functional form relating the number of health insurance carriers and the non-Hispanic black prevalence of a market might provide a better fit. eTable 5 indicates qualitatively similar results to our main results. Additionally, while the quadratic terms are broadly statistically significant, the model fit is improved only marginally, if at all. As such, we report the results from the simpler (linear) functional form specification within our main results of the paper.

Analysis
Rather than present results as stratified by specialty, eTable 6 presents pooled results across specialty. In columns (1) and (2) we do not allow for any heterogeneity across specialties, however, in columns (3) and (4) this heterogeneity is allowed by the inclusion of specialty interactions (where pediatrics represents the omitted specialty category) with the non-Hispanic black prevalence measure. Columns (3) and (4) indicate significant heterogeneities across specialties, and results are qualitatively similar to our main (specialty stratified) results within the text. Note: Robust standard errors are reported within the in parentheses; statistical significance is denoted as: a p<0.01, b p<0.05, c p<0.1. Controls are included in a sequential fashion as within Table 2 of the main text; and the same controls are used here as within Table 2 of the main text.